Interpreting historical ICU data using associational and temporal reasoning

Abstract

Medical staff in the Intensive Care Unit (ICU) are confronted with large volumes of continuous data from several physiological sources which require interpretation. The ASSOCIATE system analyses historical data for summarisation and patient state assessment. It uses a temporal expert system based on associational reasoning and applies three consecutive processes: filtering, which is used to remove noise; interval identification to generate temporal intervals from the filtered data - intervals which are characterised by a common direction of change (i.e. increasing, decreasing or steady); and interpretation which performs summarisation and patient state-assessments. Using the temporal intervals, interpretation involves differentiating between events which are clinically insignificant and events which are clinically significant and determining the outcome of therapy. Inherent in this process is the trend template which is used to represent events. Trend templates support temporal reasoning, knowledge to differentiate between events and taxonomical knowledge. Algorithms which are analogous to the way clinicians identify events use these trend templates.